The New Fuzzy SWOT: Empirical Application with Expertons

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TheNewFuzzySWOT:EmpiricalApplication
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DOI:10.7200/esicm.156.0481.3ee
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Abstract
The objective of this work is to analyze and improve the SWOT technique, considered
one of the most classic analytical techniques in the field of strategy. The results obtained
show how an instrument of fuzzy logic can measure the degree of strength of factors that
correspond to the strengths, weaknesses, opportunities, and threats which correspond to
a particular sector: in this case, that of the pharmaceutical sector of Paraguay. Its original-
ity consists of working with Expertons, an instrument which maintains the information
of all studied experts intact in order to obtain a better result. From this, a model which
captures and measures the level of importance of each of the SWOT factors is developed.
Future lines of investigation are suggested which integrate Fuzzy-SWOT and Fuzzy-Del-
phi to work with classic fuzzy numbers, triangular fuzzy numbers, and trapezoidal fuzzy
numbers in order to obtain a more complete and complex Fuzzy-SWOT model which
better reflects reality.
Keywords: Fuzzy Logic, SWOT, Theory of Expertons, Strategic Marketing.
JEL codes: D8, M30, I11.
Esic Market Economics and Business Journal
Vol. 48, Issue 1, January-April 2017, 9-29
The New Fuzzy SWOT:
Empirical Application with Expertons
Adriana Santos Caballero*
Jaume Gil Lafuente
University of Barcelona
* Corresponding author. Email: hadrisant@hotmail.com
ISSN 0212-1867 / e-ISSN 1989-3558
© ESIC Editorial, ESIC Business & Marketing School
DOI: 10.7200/esicm.156.0481.1i
http://www.esic.edu/esicmarket

10 Adriana Santos Caballero and Jaume Gil Lafuente
1. Introduction
The objective of this study is to develop a model which is able to analyze and
improve the classic SWOT strategic technique in the context of a particular case
in the industry of pharmaceutical manufacturing. The purpose of such a model is,
on the one hand, descriptive, given that it attempts to answer the question as to
“which” variables essentially form the selection process of corresponding SWOT
matrix factors (Bacharach, 1989). On the other hand, its purpose is also construc-
tive, building from observation of the basic conditions of the industry in question
with respect to the paradigm of industrial analysis: STRUCTURE-BEHAVIOR-RE-
SULTS (Scherer, 1980).
The competitive setting contains factors that influence upon certain types of busi-
nesses, in a particular industry, defined by Porter & Miller (1996) as that which is
“made up of a group of businesses which make products (or services) which may be
substituted among them. It forms the negotiation in which the business moves, and
therefore it is what permits it to identify who its clients and its competitors are, and
which are its markets” (p. 20). Given this, the current paper concentrates on one
sector specifically: the Paraguayan pharmaceutical industry.
For many years, the SWOT analysis has been sought out to identify the strengths,
weaknesses, opportunities, and threats to a business. However, this type of analysis
is static and rarely results in the development of clear alternative strategies (Jiménez,
2011). Consequently, the SWOT matrix was introduced to analyze the competitive
situation of a business, which allows four defined groupings of alternative strategies
to be identified (Koontz-Weirich, 2004).
Many published articles contribute little or nothing to furthering the principle
purpose of SWOT analysis, limiting themselves to commentaries on what each of the
four categories can include. In summary, they explain “how to” but not “how the
results can be used”, which is the most important aspect (Jiménez, 2011).
From here springs the principle objective of this work: to develop a model
through the application of fuzzy logic in which the internal and external variables of
the SWOT matrix can be measured using Expertons. This distinguishes the classic
SWOT from the Fuzzy SWOT.
The fuzzy logic methodology has been selected because the dramatic changes
experienced by humanity at the end of the 20th and the beginning of the 21st century
require a new way of viewing the application of some linear tools, themselves of
dated structures, and the adoption of new instruments to address imprecise, uncer-
tain, and diffuse realities (Rico & Tinto, 2008). A body of theory that may present a
solution is found within fuzzy mathematics, originally in Zadeh (1965) in his article
titled Fuzzy Sets, in which he introduces and develops the theory of fuzzy sets, which
have been adapted in various models to incorporate uncertainty and subjectivity in
the treatment of problems in economics and business administration studies.
Considering that Fuzzy Logic has different instruments which could be applied
to SWOT, as a starting point this work has selected “That of the Experton”, due to

The New Fuzzy SWOT: Empirical Application with Expertons 11
its idealness; group decisions refer to decision processes in which various people and
experts intervene (Kaufmann & Gil, 1992). They are useful for increasing the credi-
bility of information, as in general terms, it is supposed that the opinion of a group
of (expert) people tends to be more reliable than the opinion of one (Kaufmann &
Gil, 1992).
2. Theoretical Framework
To evaluate this study the Journal of Citation Reports and the Web of Science
have been selected, both belonging to ISI Web of Knowledge, keeping in mind that
these collect international scientific journals and articles. They are used in database
form as a support to better explore the state of the art. Using this database, papers
specializing in fuzzy logic and the SWOT technique in marketing can be located. One
of the objectives of this project is to identify studies which have been carried out
throughout time in order to demonstrate the importance of these topics for future
lines of scientific inquiry.
2.1. Analysis of Fuzzy Logic
This theory was the starting point of multiple studies and led to fuzzy control
technology, developed mainly in Japan. In this respect, Gil Aluja (2000) claims that
the work of Zadeh (1965) was the principle instigator of a change of direction in
social science research. The academics Kaufmann and Gil Aluja generated long and
numerous studies on this topic, forging a change in direction in the way of address-
ing searches for scientific explanations in the social sciences and stimulating the
incorporation of new authors in the study of fuzzy logic, such as those cited by Gil
Aluja (2000).
A database search of the keyword “fuzzy” returns a total of 193,405 entries.
The total quantity of entries by document type: Articles (118,788), Theses or Dis-
sertations (22), Editorial Material (857), Reviews (1,344), Letters (539), and others.
Table 1 shows the evolution of the number of articles published with the keyword
“Fuzzy” between 1980 and 2014.
• Web of Science: From Jan 1, 1980 to Dec 30, 2014.
• Keyword: Fuzzy.
Below the results of the search are shown:

12 Adriana Santos Caballero and Jaume Gil Lafuente
Table 1. Development of research on Fuzzy Logic (Self-elaboration from data from Web of
Science (ISI), 1980 – 2014)
Year Articles Year Articles Year Articles
1980 176 1992 1729 2004 9014
1981 166 1993 2200 2005 10067
1982 174 1994 2546 2006 11953
1983 211 1995 2835 2007 13130
1984 222 1996 3382 2008 14933
1985 285 1997 3704 2009 16748
1986 269 1998 6028 2010 9867
1987 286 1999 5371 2011 10558
1988 389 2000 6663 2012 11824
1989 471 2001 7061 2013 12873
1990 690 2002 7282 2014 11157
1991 1152 2003 7989
Total = 193,405
In the period analyzed, corresponding to the years 1980 to 2014, according to
the executed search, 33,359 articles were published in Latin America on fuzzy, with
the United States being the country which makes the greatest use of fuzzy logic,
followed by Canada and Brazil. The target country of this study currently maintains
zero publications. This is one of the reasons for applying the fuzzy technique to the
pharmaceutical industry of that country: to thereby disseminate it and increase its
exposure.
Table 2. Authors related to Fuzzy Logic and its differing techniques (Self-elaboration)
Authors Contribution through Fuzzy Logic Instrument
López and
Mendaña (2001)
Determination of the best tool for car-
rying out management control for the
liquidation of a company.
Development of a comparative analy-
sis between Markov stochastic chains,
as a tool for treating information in
business management which use ran-
dom variables and chaining through
associated sum-product operators and
uncertain chains of Kaufmann and Gil
Aluja (1992).
Andrés and
Terceño (2002)
Study on the actuarial applications of
Fuzzy Sets, exploring different possi-
bilities of the application of this theory.
Implementation of Fuzzy Models.

The New Fuzzy SWOT: Empirical Application with Expertons 13
Authors Contribution through Fuzzy Logic Instrument
Reig and
González (2002)
Planning and tactical control of materi-
als in the area of business provisioning,
“incorporating quality of the informa-
tion used as a model parameter”.
The use of the Expertons technique,
which permits intrinsic subjectivity
to be objectified, facilitating its rep-
resentation through true values, confi-
dence intervals, and confidence triplets.
González (2006) Selection of a role in a messaging busi-
ness, carrying out the characterization
of attributes, establishing the level of
particularities, and defining the fuzzy
set which represents the description of
the ideal required role.
Fuzzy Subsets
González, Flores,
and Chagolla
(2006)
Methodological proposal for selecting
personnel and application to a practi-
cal case.
Hamming and Euclidian distance, par-
ticipation of experts, and the assign-
ment of values to estimations in the
interval [0,1].
Gil Lafuente,
Ortigosa, and
Merigó (2007)
Models for obtaining customer value,
considering their loyalty to the busi-
ness, and introducing uncertainty.
Confidence and triple intervals for the
following variables: interest rate, sales,
and costs.
Gil Lafuente and
Tinto (2007)
Selection process of organizational
personnel (selection, targeting, and
substitution of professional players in
various sports).
Trapezoidal fuzzy number, confidence
intervals, random fuzzy subsets, Exper-
tons, counter-expertise, among others.
2.2. Analysis on the SWOT Technique
The Keyword “SWOT” was introduced, returning a total of 2,979 entries. The
total number of documents by type: Articles (1867), Thesis Dissertation (3), Edito-
rial Material (25), Review (65), Letter (7) and others.
In Table 3, the evolution of published articles with the keyword “SWOT”
between the years of 1980 and 2014 is presented.
• WebofScience:From01-01-1980to30-12-2014.
• Keyword:SWOT.
Below the results of the search are presented:
Table 2. (continuation)

14 Adriana Santos Caballero and Jaume Gil Lafuente
Table 3. Development of research on the SWOT Technique (Self-elaboration from data from
Web of Science (ISI), 1980 – 2014)
Year Articles Year Articles Year Articles
1980 1992 22004 84
1981 1993 62005 89
1982 1994 11 2006 109
1983 11995 13 2007 136
1984 1996 17 2008 210
1985 11997 19 2009 256
1986 1998 15 2010 307
1987 11999 19 2011 351
1988 12000 32 2012 388
1989 32001 28 2013 417
1990 22002 46 2014 357
1991 32003 56
Total = 2.979
The number of articles about SWOT has steadily increased, especially since the
year 2000.
The initials “SWOT” correspond to strengths, weaknesses, opportunities, and
threats. SWOT analysis consists in evaluating strong and weak factor which, col-
lectively, diagnose the internal situation of an organization, as well as the external
situation, i.e., the opportunities and threats (Jiménez, 2010). It can also be consid-
ered a simple tool which allows one to gain a general perspective of the strategic
situation of a particular organization. Thompson and Strikland (1998) establish that
SWOT analysis estimates the effect that a strategy has in order to achieve a balance
or adjustment between internal organizational capacities and its external situation,
that is, the opportunities and threats.
In the literature, a number of contradictions appear between different points of
view around the origin of the analysis:
Table 4. Authors related to the SWOT Technique (Self-elaboration)
Author(s) Attributions
Dealtry (1992) Considers SWOT in terms or groups of vectors with common aspects and in-
teractions.
Wheelan and
Hunger (1998)
Utilized SWOT to detect gaps and meeting points between capacities and re-
sources of the organization in the business setting.

The New Fuzzy SWOT: Empirical Application with Expertons 15
Author(s) Attributions
Koch (2000) While examining the contributions of Weihich (1982), Dealtry (1992), and
Wheelan and Hunger (1998), the author demonstrates that the most common
observer would be capable of recognizing that Weihrich (1982) was not the
creator of the concept, but rather built upon it.
Turner (2002) Attributes SWOT to Igor Ansoff (1987), of Ansoff’s Matrix.
King (2004) Recognizes that it was difficult to trace the origins of the initials SWOT. Cites
Haberberg (2000) when he declares that SWOT was a concept introduced by
academics at Harvard in the 1960s.
Koch (2004) Comments that he recognized that SWOT analysis had the advantage of being
an arbitrary, unique matrix.
Shinno et al. (2006) Utilize a software and combine the SWOT analysis with an analytical hierar-
chical process (AHP) which situates and prioritizes each element, but in reality
does impact the obvious limitations of SWOT.
2.3. How to identify the factors that make up the SWOT matrix?
Opportunities consist of environmental forces of an external character which are
out of the organization’s control, but which represent potential growth or improve-
ment elements. The opportunity in the middle is a factor of great importance which
permits the shaping of organizational strategy. Threats are the opposite of this, and
represent the sum of uncontrollable environmental factors, but which also represent
negative forces or aspects and potential problems. Opportunities and threats can
not only influence the appeal of the state of the organization, but rather they can
also stablish the need to take actions of a strategic character—the important factor
in this analysis is to evaluate its strengths and weaknesses (Thompson & Strikland,
1998).
Table 4. (continuation)

16 Adriana Santos Caballero and Jaume Gil Lafuente
Table 5. SWOT Matrix (Thompson and Strikland, 1998)
Strengths Weaknesses
Fundamental capacity in key areas.
Sufficient financial resources.
Good buyer image.
Being recognized as a market leader.
Well-reasoned strategy in functional areas.
Access to economies of scale.
Isolation (at least to a certain degree) from strong
competitive pressures.
Cost advantages.
Superior marketing campaigns.
Abilities in product innovation.
Capable leadership.
Advantageous position in the experience curve.
Superior capacity of production.
Superior technical skill.
No clear strategic direction.
Obsolete installations.
Below-average profitability.
Lack of opportunity and management talent.
Inefficient follow-up when executing strategy.
Abundance of internal operative problems.
Delays in research and development.
Overly limited product line.
Weak image in the market.
Weak distribution capability.
Market technology skill below average.
Unable to finance strategically necessary changes.
Unitary costs higher than key competitors.
Opportunities Threats
Attend to new client groups.
Invest in new markets or market segments.
Expand the product line to satisfy a wider range
of client needs.
Diversify into related products.
Vertical integration (forward or backward).
Elimination of trade barriers in attractive foreign
markets.
Complacence among smaller rivals.
Faster market growth.
Entry of foreign competitors with lower costs.
Increase in sales of substitute goods.
Slower market growth.
Adverse changes in exchange rates or trade poli-
cies in foreign markets.
Costly regulations.
Vulnerability of recession and business cycle.
Growing power of clients and suppliers.
Changes in buyer needs and preferences.
Adverse demographic change.
3. Methodology
3.1. Sample
The sample was collected with 37 laboratories that form a part of the Chamber
of Pharmaceutical Industries (CIFARMA) in mind, considering that only 10 have
sufficiently large and appropriate infrastructures to export medication according to
international law. 10 representatives of these 10 laboratories were selected, keeping
in mind that these are the most representative of the industry.

The New Fuzzy SWOT: Empirical Application with Expertons 17
3.2. Data Collection
Figure 1. Stages of implementation of the Model (Self-elaboration)
3.2.1. Stage 1: Statistical Information (Environmental factors of the business)
Data was obtained from primary sources and the design and application of a
survey were necessary (designed and adapted from the SWOT Matrix of Thompson
and Strikland (1998)) which as a statistical method permitted the integration of the
principles of Fuzzy Logic in the collection of data and to identify the ratings from
each of the 10 selected experts. All of the questions were designed as questions in the
form of Expertons in order to be able to capture the personal opinion of the surveyed
experts on the area of study and thereby obtain the necessary expertise. The instru-
ment was applied in 10 pharmaceutical businesses located in Asunción, the capital
and most populous city of the Republic of Paraguay.
3.2.2. Stage II: Measurement of Variables
As a first step, using the literature review each distinct internal and external fac-
tor of the Paraguayan pharmaceutical industry was enumerated in order to analyze
its competitive profile. Considering this, the second step was to apply the SWOT
matrix using the factors given by Thompson and Strikland (1998).
Stage I
Stage II
Stage III
• Measurement
of Variables.
• Applicationto
the Field Case.
• Environmental
factors of the
business.

18 Adriana Santos Caballero and Jaume Gil Lafuente
Table 6. SWOT matrix with factors analyzed by experts (Adapted from Thompson and
Strikland, 1998)
Strengths Weaknesses
S1
S2
S3
S4
S5
S6
S7
Fundamental capacity in key areas.
Sufficient financial resources.
Access to economies of scale.
Cost advantages.
Superior marketing campaigns.
Capable leadership.
Being recognized as a market leader.
W1
W2
W3
W4
W5
W6
W7
Obsolete installations.
Lack of opportunity and management
talent.
Delays in research and development.
Weak image in the market.
Weak distribution capability.
Market technology skill below average.
Below-average profitability.
Opportunities Threats
O1
O2
O3
O4
O5
O6
O7
Diversify into related products.
Attend to new client groups.
Invest in new markets or market segments.
Expand the product line to satisfy a wider
range of client needs.
Elimination of trade barriers in attractive
foreign markets.
Complacence among smaller rivals.
Faster market growth.
T1
T2
T3
T4
T5
T6
T7
Increase in sales of substitute goods.
Slower market growth.
Adverse changes in exchange rates or trade
policies in foreign markets.
Costly regulations.
Vulnerability of recession and business
cycle.
Growing power of clients and suppliers.
Adverse demographic change.
3.2.3. Stage III: Application to the Field Case
The next stage was to select the 10 businesses of the pharmaceutical industry in
Paraguay, in order to later contact their respective marketing representatives to be
able to count on the collaboration of each of the experts in the Fuzzy-SWOT analysis.
The collection of field data took place during the months of April and May, 2015.
Once the survey was carried out, each of the experts gave their ratings with
respect to each factor, in order to perform the Experton calculations. The 10 labora-
tories of the expert participants represent 71.2% of the total income of the industry
in this sector. Taking this into account, we consider the sample to be sufficiently
representative of the population, which allows the study to be carried out and con-
clusions established.

The New Fuzzy SWOT: Empirical Application with Expertons 19
Carrying out the expertise step by step results in Expertons for 7 of the factors of
study corresponding to each quadrant of the SWOT matrix.
4. Data Analysis
To analyze the data, the instruments of Fuzzy Logic were used: the Theory of
Expertons, followed by the minimization and maximization of Expertons, and final-
ly the comparison of the confidence interval. Through these techniques we move
from a classic SWOT Matrix to Fuzzy-SWOT, in this case using the factors of the
matrix of Thompson and Strikland (1998).
4.1. General Theory of Expertons (I)
In the first instance, and given that it will be used later on, it is necessary to point
out that one of the essential tools used in fuzzy logic to lessen entropy and refine
analyzed values is the semantic endecadaria scale, which is adapted to eleven lin-
guistic expressions—there could be more—and is therefore subjective and uncertain,
but with a sensible level of presumption α of truth comprised in the interval [0 ; 1],
where the zero represents unequivocal not belonging and the one absolute belonging.
In that order of ideas, expertise is defined as the process of consultation of experts in
relation to a particular theme, in order to delimit uncertainty (Hurtado, 2011). In the
same line, expert is understood as an individual with appropriate abilities and skills,
and sufficiently capacitated in the subject of interest thanks to academic, profession-
al, or empirical experience (Medina, 2006). As much as possible it is important to
select different groups of experts and to communicate questions individually, with-
out encouraging rivalries between them and eliminating any incentive to lie (Andreu
& Ceballos, 2005) to guarantee the trustworthiness of the expert sample.
Additionally, the result of the mathematical process through which the informa-
tion provided by the experts is evaluated in accordance with incidents of cause–effect
or effect–cause is called Experton, which allows knowledge to be aggregated using
the interaction of the responses of all of the experts (Kaufmann & Gil, 1992).
4.2. Minimization and Maximization of Expertons (II)
Given two Expertons A and B ⊂ E
Minimum: A(∧)B = C ⇔ ∀α ∈ [0,1]
Maximum: A(∨)B = C ⇔ ∀α ∈ [0,1]

20 Adriana Santos Caballero and Jaume Gil Lafuente
4.3. ComparingConfidenceIntervals(StageIII)
According to the nature of ratios which are being studied, the necessary compar-
ison type can be discovered, the ordination of the intervals as increasing or decreas-
ing, and the comparison of the obtained ratio with certain measures (Gil, 1999). The
first case reveals the largest as well as the smallest confidence interval which can be
formed with the given intervals; in order to achieve this one looks to the operators ∨,
which represents “the greatest between” (take the largest), and ∧, which represents
“the minimum between” (take the smallest) (Kaufmann & Gil, 1992). Thus two
limits are obtained.
The upper-limit (supremum): obtained by selecting between the lower extremes
the larger of the limits and between the upper extremes the larger. The objective is to
obtain an interval situated more to the right, but which does not represent the widest
nor the one of the most uncertainty (Gil, 1999).
4.4. Hamming Distance (IV)
Using the Hamming Distance the different confidence intervals are compared and
ordered with an “ideal” profile identified, because it allows the visualization of the
range of each of the variables of study in function of its ideal values (Gil, 1999). In
sum, this group of fuzzy logic tools demonstrates the capacity to collect indicators
and perspectives considered fundamental for a study of SWOT. In the developed
model expertise is used to identify confidence intervals which describe the real value
of each selected variable for the study of the factors corresponding to the SWOT
matrix.
Follow the procedures of Stage I, II, III, and IV, reveals the order of the ratio
totals and a visualization of how distant the studied variables are from the interval
which represents the upper and lower limits. In the same way, it offers a way to
order and compare the distinct confidence intervals with an “ideal” profile previous-
ly identified, as it permits one to visualize the range of each of the variables under
investigation in function of its ideal value (Gil, 1999). In summary, from this group
of fuzzy logic tools one can arrive at a compilation of indicators and viewpoints con-
sidered fundamental for SWOT analyses. In the developed model, expertise is used
to identify confidence intervals that describe the real value of each selected variable
for the study of the corresponding factors of the SWOT matrix.
The distance expresses the degree of separation from a “perfect factor”, that
factor with the greatest importance. The greater the distance, the less relevant that
factor for study.

The New Fuzzy SWOT: Empirical Application with Expertons 21
Table 7. Results of Expertons for strength factors (Self-elaboration)
Strengths S1 S2 S3 S4 S5 S6 S7
0 1 1 1 1 1 1 1
0.1 1 1 1 1 1 1 1
0.2 [0.6, 1] [0.9, 1] 1 1 1 1 1
0.3 [0.9, 1] [0.7, 1] 1 1 1 1 [0.9, 1]
0.4 [0, 1] [0.6, 1] [0.9, 1] [0.6, 1] 1 1 [0.9, 1]
0.5 [0, 1] [0.5, 1] [0.7, 1] [0.4, 1] [0.8, 1] [0.8, 1] [0.8, 1]
0.6 [0, 1] [0.2, 1] [0.4, 1] [0.3, 0.9] [0.4, 0.9] [0.6, 0.9] [0.7, 1]
0.7 [0, 1] [0, 1] [0.4,0.9] [0.2,0.7] [0.1, 0.7] [0.3, 0.9] [0.6, 0.9]
0.8 [0, 0.9] [0, 1] [0.1, 0.5] [0.1, 0.6] [0.1, 0.5] [0.2, 0.7] [0.5, 0.7]
0.9 [0, 0.9] [0, 1] [0.1, 0.3] [0, 0.4] [0.1, 0.4] [0.1, 0.5] [0.3, 0.6]
1 [0, 0.6] [0, 0.5] [0.1, 0.2] [0, 0.2] [0.1, 0.2] [0, 0.2] [0.2, 0.4]
Table 8. Description of each strength, with corresponding calculations (Adapted from
Thompson and Strikland, 1998)
Strengths Factor Distance of Hamming Average(DH)
Fundamental capacity in key areas. S1 [0.45 , 0.01] 0.115
Sufficient financial resources. S2 [0.34 , 0.01] 0.875
Access to economies of scale. S3 [0.14 , 0.17] 0.08
Cost advantage. S4 [0.25 , 0.18] 0.10
Superior marketing campaigns. S5 [0.15 , 0.28] 0.1075
Capable leadership. S6 [0.14 , 0.15] 0.072
Being recognized as a market leader. S7 [0.02 , 0.1] 0.075
S6<S7<S3<S2<S4<S5<S1
Table 9. Results of Expertons for weaknesses factors (Self-elaboration)
Weaknesses W1 W2 W3 W4 W5 W6 W7
0 1 1 1 1 1 1 1
0.1 1 1 1 1 1 1 1
0.2 1 1 1 1 1 1 1
0.3 1 [0.9, 1 ] [0.8, 1] 1 1 1 [0.9, 1]
0.4 [0.8, 0.1] [0.8, 1] [0.8, 1] [0.7, 1] 1 [0.9, 1 ] [0.9, 1]
0.5 [0.6, 1] [0.7, 1] [0.7, 1] [0.4, 1] 1 [0.8, 1 ] [0.9, 1]

22 Adriana Santos Caballero and Jaume Gil Lafuente
Weaknesses W1 W2 W3 W4 W5 W6 W7
0.6 [0.4, 1] [0.6, 0.9] [0.7, 0.9] [0.2, 1] [0.7, 1] [0.7, 1] [0.8, 1]
0.7 [0.3, 1] [0.3, 0.9] [0.5,0.9 ] [0.2,0.7] [0.5, 1] [0.6, 1 ] [0.8, 0.9]
0.8 [0.3, 0.7] [0, 0.6] [0.3, 0.7] [0, 0.4] [0.2, 0.9] [0.3, 0.8] [0.5, 0.9]
0.9 [0.2, 0.6] [0, 0.3] [0.2, 0.7] [0, 0.3] [0.1, 0.7] [0.1, 0.7] [0.3, 0.9]
1 [0.1, 0.4] [0, 0.1] 0.2 [0, 0.1] 0 [0, 0.3] [0.3, 0.4]
Table 10. Description of each weaknesses, with corresponding calculations (Adapted from
Thompson and Strikland, 1998)
Weaknesses Factor Distance of Hamming Average(DH)
Obsolete installations. W1 [0.20 , 0.05] 0.0625
Lack of opportunity and
management talent. W2 [0.24 , 0.15] 0.0975
Delays in research and development. W3 [0.15 , 0.09] 0.06
Weak image in the market. W4 [0.30 , 0.16] 0.115
Weak distribution capability. W5 [0.12 , 0.07] 0.0475
Market technology skill below average. W6 [0.13 , 0.05] 0.045
Below-average profitability. W7 [0.03 , 0.01] 0.01
W7<W6<W5<W3<W1<W2<W4
Table 11. Results of Expertons for opportunities factors (Self-elaboration)
Opportunities O1 O2 O3 O4 O5 O6 F7
0 1111111
0.1 1 1 1 1 1 1 1
0.2 1 1 1 1 1 [0.9, 1 ] [0.9, 1]
0.3 [0.9, 0.1] 1 1 1 1 [0.9, 1 ] [0.9, 1]
0.4 [0.6, 1] [0.9, 0.1] 1 [0.7, 1] 1 [0.9, 1 ] [0.9, 1]
0.5 [0.6, 1] [0.8, 1] [0.8, 1] [0.4, 1] 1 [0.7, 1 ] [0.7, 1]
0.6 [0.6, 0.9] [0.5, 1] [0.5, 0.7] [0.2, 1] [0.7, 1] [0.5, 0.9] [0.5, 0.9]
0.7 [0.5, 0.7] [0.5, 1] [0.1,0.7 ] [0.2,0.7] [0.5, 1] [0.2, 0.8] [0.3, 0.9]
0.8 [0.3, 0.6] [0.2, 0.7] [0.4, 0.6] [0, 0.4] [0.3, 0.9] [0.3, 0.8] [0.2, 0.8]
0.9 [0.1, 0.6] [0, 0.5] [0.1, 0.4] [0, 0.3] [0.2, 0.8] [0, 0.4] [0, 0.4]
1 [0.1, 0.5] [0, 0.1] [0.1, 0.3] [0, 0.1] [0, 0.1] [0, 0.3] [0, 0.3]
Table 9. (continuation)

The New Fuzzy SWOT: Empirical Application with Expertons 23
Table 12. Description of each opportunities, with corresponding calculations (Adapted from
Thompson and Strikland, 1998)
Opportunities Factor Distance of Hamming Average(DH)
Diversify into related products. O1 [0.12 , 0.09] 0.0525
Attend to new client groups. O2 [0.10 , 0.09] 0.0475
Invest in new markets or market
segments. O3 [0.11 , 0.15] 0.065
Expand the product line to satisfy a
wider range of client needs. O4 [0.24 , 0.18] 0.105
Elimination of trade barriers in
attractive foreign markets. O5 [0.02 , 0.04] 0.015
Complacence among smaller rivals. O6 [0.18 , 0.09] 0.052
Faster market growth. O7 [0.09 , 0.15] 0.06
O5<O2<O6<O7<O1<O3<O4
Table 13. Results of Expertons for threats factors (Self-elaboration)
Threats T1 T2 T3 T4 T5 T6 T7
0 1 1 1 1 1 1 1
0.1 1 1 1 1 1 1 1
0.2 1 1 1 1 1 1 1
0.3 1] 1 [0.9, 1 ] 1 1 1 1
0.4 [0.8, 1] [0.8, 0.1] [0.7, 1] 1 [0.8, 0.1] [0.9, 1 ] [0.9, 1]
0.5 [0.6, 1] [0.7, 1] [0.7, 1] [0.8, 1] [0.7, 1] [0.7, 1 ] [0.6, 1]
0.6 [0.3, 0.9] [0.5, 0.9] [0.6, 1] [0.6, 1] [0.5, 0.7] [0.3, 1 ] [0.4, 1]
0.7 [0.3, 0.7] [0.3, 0.9] [0.4,0.8 ] [0.3, 1] [0.1, 0.5] [0.1, 1 ] [0.3, 0.9]
0.8 [0.2, 0.4] [0.2, 0.7] [0.3, 0.6] [0.2,0.8] [0.1, 0.4] [0.1, 0.6] [0.2, 0.6]
0.9 [0.2, 0.3] [0.2, 0.7] [0.2, 0.5] [0, 0.5] [0, 0.3] [0.1, 0.3] [0.1, 0.4]
1 [0.1, 0.2] 0.1, 0.3] [0.1, 0.3] [0, 0.2] [0, 0.1] 0.1 [0, 0.1]
Table 14. Description of each threats, with corresponding calculations (Adapted from
Thompson and Strikland, 1998)
Threats Factor Distance of Hamming Average(DH)
Increase in sales of substitute goods. T1 [0.07 , 0.13] 0.05
Slower market growth. T2 [0.06 , 0.04] 0.025
Adverse changes in exchange rates or
trade policies in foreign markets. T3 [0.05 , 0.06] 0.0275

24 Adriana Santos Caballero and Jaume Gil Lafuente
Threats Factor Distance of Hamming Average(DH)
Costly regulations. T4 [0.05 , 0.03] 0.02
Vulnerability of recession and
business cycle. T5 [0.12 , 0.18] 0.075
Growing power of clients and suppliers. T6 [0.11 , 0.08] 0.0725
Adverse demographic change. T7 [0.08 , 0.08] 0.04
T4<T2<T3<T7<T1<T5<T6
5. Results
Table 15. The new Fuzzy-SWOT matrix (Self-elaboration)
Strengths Weaknesses
S1
S2
S3
S4
S5
S6
S7
Capable leadership.
Being recognized as a market leader.
Access to economies of scale.
Sufficient financial resources.
Cost advantages.
Superior marketing campaigns.
Fundamental capacity in key areas.
W1
W2
W3
W4
W5
W6
W7
Below-average profitability.
Market technology skill below average.
Weak distribution capability.
Delays in research and development.
Obsolete installations.
Lack of opportunity and management
talent.
Weak image in the market.
Opportunities Threats
O1
O2
O3
O4
O5
O6
O7
Elimination of trade barriers in attractive
foreign markets.
Attend to new client groups.
Complacence among smaller rivals.
Faster market growth.
Diversify into related products.
Invest in new markets or market segments.
Expand the product line to satisfy a wider
range of client needs.
T1
T2
T3
T4
T5
T6
T7
Costly regulations.
Slower market growth.
Adverse changes in exchange rates or trade
policies in foreign markets.
Adverse demographic change.
Increase in sales of substitute goods.
Vulnerability of recession and business
cycle.
Growing power of clients and suppliers.
Table 14. (continuation)

The New Fuzzy SWOT: Empirical Application with Expertons 25
6. Discussion, Conclusions, and Implications
7 distinct factors of the 4 quadrants of classic SWOT (Strengths, Weaknesses,
Opportunities, and Threats) of Thompson and Strikland (1998) have been evaluat-
ed, centering on an analysis of the internal and external settings of the Paraguayan
pharmaceutical sector.
In Tables 6 and 15, the position of the factors according to their order of weight/
importance can be seen to change through the application of the fuzzy logic tech-
nique. It is concluded that Fuzzy-SWOT, apart from being a strategic technique like
the classic SWOT, also has the capacity to measure the weight and/or importance of
each variable or factor corresponding to the SWOT matrix by employing Expertons
and confidence intervals, owing to its empirical nature.
Fuzzy-SWOT distinguishes itself from the classic model by using Expertons. Con-
sidering that Fuzzy Logic has different instruments than those that can be applied
to SWOT, in this study we began by applying that of “the Experton” due to its ide-
alness in addressing group decisions, in which various people or experts participate
in the decision making process (Kaufmann & Gil, 1992). This allows an analysis of
competition of the sector, by including experts from distinct pharmaceutical busi-
nesses of Paraguay.
The Paraguayan pharmaceutical industry was targeted for two reasons:
• Given that Porter and Miller (1986) define a competitive setting with four
factors which influence a single type of business, “those businesses which
produce products or services among themselves”.
• The search for articles in the Web of Science database with the keyword
“Fuzzy” returned zero results. Few countries in Latin American have
employed this methodology.
7. Research Limitations
The study analyzes only the SWOT matrix of Thompson and Strikland (1998).
The value of the responses of each of the experts could be biased, and it could be
appropriate to standardize them. The original objective and subjective values are not
transformed to a common scale. Empirical indicators expressed in heterogeneous
units should be studied and applied, because not doing so hinders the integration
of the numerical expression of the measures of each factor belonging to the SWOT
matrix. These indicators have not been transformed into directly integrable homog-
enous units, though doing so would simplify the data reading by presenting only one
unit of analysis.

26 Adriana Santos Caballero and Jaume Gil Lafuente
8. Future Lines of Research
Studies that integrate Fuzzy-SWOT and Fuzzy-Delphi are suggested to be able to
work with classical fuzzy numbers, triangular fuzzy numbers, and trapezoidal fuzzy
numbers, in order to achieve a more complete, more complex, and more realistic
Fuzzy-SWOT model.
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The New Fuzzy SWOT: Empirical Application with Expertons 29
Notes on Contributors
Name: Adriana Santos Caballero
Position: Doctorando en Business Research
School / Faculty: Economía y Empresa
University: Universitat de Barcelona
Address: Carrer Joan XXIII Nº 5, Corbera de Llobregat, CP. 08757
Telephone: 610849066
Email: hadrisant@hotmail.com
Name: Jaume Gil Lafuente
Position: Profesor titular del departamento de Economía y Empresa
School / Faculty: Economía y Empresa
University: Universitat de Barcelona
Address: Avenida Diagonal 690-696. CP. 08034, Barcelona España
Telephone: 610929004
Email: j.gillafuente@gmail.com

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